RSRCC：通过检索增强的最佳N排序构建的遥感区域变化理解基准
RSRCC: A Remote Sensing Regional Change Comprehension Benchmark Constructed via Retrieval-Augmented Best-of-N Ranking
AI总结：
提出RSRCC基准，包含12.6万个细粒度遥感变化问答对，采用层次化半监督流程结合最佳N排序解决歧义，实现局部语义变化推理。
摘要：
传统变化检测识别变化发生的位置，但不解释发生了什么变化。现有的遥感变化描述数据集通常描述整体图像级别的差异，而细粒度的局部语义推理尚未充分探索。为弥补这一差距，我们提出RSRCC，一个新的遥感变化问答基准，包含12.6万个问题，分为8.7万训练、1.71万验证和2.2万测试实例。与以往数据集不同，RSRCC围绕局部、变化特定的问题构建，需要推理特定的语义变化。据我们所知，这是第一个明确设计用于此类细粒度推理监督的遥感变化问答基准。为构建RSRCC，我们引入了一个层次化半监督策展流程，将最佳N排序作为关键的最后歧义解决阶段。首先，从语义分割掩码中提取候选变化区域，然后使用图像-文本嵌入模型进行初步筛选，最后通过检索增强的视觉语言策展和最佳N排序进行验证。该过程能够在保留语义有意义变化的同时，对噪声和模糊候选进行可扩展过滤。数据集可在该网址获取。
英文摘要：
Traditional change detection identifies where changes occur, but does not explain what changed in natural language. Existing remote sensing change captioning datasets typically describe overall image-level differences, leaving fine-grained localized semantic reasoning largely unexplored. To close this gap, we present RSRCC, a new benchmark for remote sensing change question-answering containing 126k questions, split into 87k training, 17.1k validation, and 22k test instances. Unlike prior datasets, RSRCC is built around localized, change-specific questions that require reasoning about a particular semantic change. To the best of our knowledge, this is the first remote sensing change question-answering benchmark designed explicitly for such fine-grained reasoning-based supervision. To construct RSRCC, we introduce a hierarchical semi-supervised curation pipeline that uses Best-of-N ranking as a critical final ambiguity-resolution stage. First, candidate change regions are extracted from semantic segmentation masks, then initially screened using an image-text embedding model, and finally validated through retrieval-augmented vision-language curation with Best-of-N ranking. This process enables scalable filtering of noisy and ambiguous candidates while preserving semantically meaningful changes. The dataset is available at https://huggingface.co/datasets/google/RSRCC.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
